Preparing for Modeling Requirements in Basel II: Model Development
:The Basel II Capital Accord, ready for implementation around 2007,
sets out detailed analytic requirements for risk assessment that will be
based on data collected by banks throughout the life cycle of the loan.
The purpose of Basel II is to introduce a better risk-sensitive capital
framework with incentives for good risk management practices. This article,
the first in a four part series, will give a managerial overview of
statistical modeling - an area of analytics that is paramount to the
Basel framework.

An Introduction to Survival Analysis in Business
As the field of credit scoring is focused on predicting 'if' an account will become delinquent over a certain span of time, Survival Analysis can tell us 'when.' Survival Analysis is called different things in different industries: event history analysis, reliability analysis, time to failure, and even duration analysis. The purpose of this article is to give an introduction to the subject with an emphasis on how it can be used in banking and finance.

Preparing for Modeling Requirements in Basel II: Stress Testing
The idea of stress testing should be of interest to banks not only because of Basel, but because developing such approaches can provide the institution with the tools necessary to better manage their capital. This article will introduce a modeling approach called 'Pooled Cross Section Time Series Analysis' that uses aggregated data to predict the impact of economic and portfolio changes on bank default losses.

Modeling Complex Data with Neural Networks This paper discusses Neural Networks as a prediction tool for probability of default (PD) and
recovery (LGD) models. Points of interests include terminology, strengths, weaknesses, and what to look for in a good NN package.

Thanks for visiting the WORLD OF FORECASTING! The
intent of this site is to promote an understanding of Econometrics and Multivariate Statistics in credit scoring, analytics, prospect marketing, and forecasting to the business community. Feel free to send me an email
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For years much of my webpage has been SAS oriented. However, given the high cost of SAS, I have added a resource for the free open source statistical
package called R. From the internet, Google Rstudio and R and download both to use
the programs provided in this section. Although skeptical at first, I would now argue that R does alot of things with less code than SAS, which benefits the statistican in many ways. Moreover, you can do trees, random forests, and text mining in R for free. In SAS, you would have to pay big for these extra packages. It also does thematic mapping at the national, state, zip code and census tract level with much less code than SAS. On the other hand, SAS seems much better at econometric forecasting and handling millions of records. Having said all that, please take a look at the free R scripts I have created and I think you will be amazed at your new capabilities at zero cost!
my free R scripts in this download area.